WPS3716
Trade Credit and Bank Credit:
Evidence from Recent Financial Crises
Inessa Love, Development Research Group, World Bank
Lorenzo A. Preve, IAE ­ Universidad Austral
Virginia Sarria-Allende, IAE ­ Universidad Austral
ABSTRACT
This paper studies the effect of financial crises on trade credit in a sample of 890 firms in six emerging
economies. We find that although provision of trade credit increases right after the crisis, it consequently
collapses in the following months and years. We observe that firms with weaker financial position (for
example, high pre-crisis level of short-term debt and low cash stocks and cash flows) are more likely to
reduce trade credit provided to their customers. This suggests that the decline in aggregate credit provision
is driven by the reduction in the supply of trade credit, which follows the bank credit crunch. Our results
are consistent with the "redistribution view" of trade credit provision, in which bank credit is redistributed
via trade credit by the firms with stronger financial position to the firms with weaker financial stand.
World Bank Policy Research Working Paper 3716, September 2005
The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the
exchange of ideas about development issues. An objective of the series is to get the findings out quickly,
even if the presentations are less than fully polished. The papers carry the names of the authors and should
be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely
those of the authors. They do not necessarily represent the view of the World Bank, its Executive Directors,
or the countries they represent. Policy Research Working Papers are available online at
http://econ.worldbank.org.
This paper was written as part of the dissertation of Lorenzo Preve (University of Texas, Austin) and
Virginia Sarria-Allende (Columbia Business School). We thank Andres Almazan, Charles Calomiris,
Raymond Fisman, Jay Hartzell, Charles Himmelberg, Laurie Simon Hodrick, Patrick Honohan, Ross
Jennings, Andrei Kirilenko, Pamela Moulton, Bob Parrino, Mitchell Petersen, Francisco Perez Gonzalez,
Sheridan Titman, Roberto Wessels, and all participants at the UT seminar and the World Bank seminar for
all their helpful comments and suggestions.
The financial crises experienced by emerging markets during the nineties present
extreme cases of collapse of institutional financing and, consequently, can be useful in
studying the role of alternative sources of financing during periods of severe monetary
contraction. Previous evidence suggests that trade credit could play an important role by
compensating for unavailable bank credit. Petersen and Rajan (1997) find that firms with
weaker banking relations use more trade credit; Nilsen (2002) shows that small firms and
large firms without bond ratings increase reliance on trade credit during monetary
contractions; Fisman and Love (2003) argue that in countries with undeveloped financial
intermediaries trade credit provides an alternative source of funds, which allows higher
growth rates in industries that can be characterized as intense trade credit users. Wilner
(2000) claims that suppliers tend to assist customers in distress to maintain long-term
commercial relationships. This paper contributes to the previous literature by studying the
ability of trade credit to provide an alternative source of financing during financial
crises.1
We study the effects of the 1997 Asian Crisis on firms operating in Indonesia,
South Korea, Malaysia, Philippines and Thailand, and the impact of the 1994 Peso
devaluation on Mexican firms. We create a panel of about 890 large, publicly traded
firms, and analyze their trade credit behavior around the crisis time. We are most
interested in examining whether trade credit can compensate for contracted bank credit
during the credit crunch that characterizes financial crises.
We find that trade credit provided and received by the firms in our sample
increases immediately after the crisis. More surprisingly, we find that the trade credit
1Papers by Krugman (1999), Aghion, Bacchetta and Banerjee (2000a, b and 2001), Chang and Velasco
(1999), Ding, Domac and Ferri (1998), Ding and Domac (1998), Calomiris and Beim (2001) and Bris at al.
(2002) among others study financial crisis.
1
provided by our firms (as opposed to what they receive) collapses in the aftermath of the
crisis and continues to contract for several years. Because we measure trade credit
provided as a ratio of accounts receivable to sales, the decline in credit provision is not
simply driven by decline in sales. In other words, we find that credit provided by our
firms declines more than their sales, in percentage terms.
As in any study of credit provision, the interpretation of our results is inherently
difficult because of the familiar "identification problem". The prolonged decline in trade
credit provided by the firms in the aftermath of the crisis could be due to the
unwillingness of customers to take on more credit (a demand effect), or to the inability of
the suppliers of goods to provide such a credit (a supply effect). Thus, a declining pattern
of trade credit provision does not automatically mean that trade credit cannot play an
important role in compensating for contracted bank credit.2
To understand what is driving the equilibrium patterns of trade credit, we study
firms' heterogeneous responses to the crisis events. More specifically, we analyze trade
credit policy as a function of firms' relative financial health. If the reduction of trade
credit provision is significantly higher for firms that have weaker financial conditions, it
would imply that the contraction of such credit is most likely driven by a supply effect.
Using several crisis-specific indicators of firms' relative financial strength, we find that
this is indeed the case.
The main financial indicator we use is reliance on short-term debt. Firms with
high share of short-term debt are likely to be the most disadvantaged by the crises, due to
increased interest rates and difficulties in rolling over their debts. We observe that before
2In other words, if the trade credit provided in equilibrium falls after the crises because customers demand
less trade credit, then this pattern does not say anything about the ability of trade credit to compensate for
contracted bank credit. If, on the other hand, trade credit provision falls because firms reduce their credit
2
crises, when short-term debt is abundant and (relatively) cheap, firms with higher
percentage of short-term debt provide more credit to their customers and rely less on
credit from suppliers. However, after the crisis, these firms face a disadvantaged financial
position, and consequently they significantly cut the credit extended to their customers,
and use more credit from suppliers. That is, firms with higher reliance on short-term debt
are the main suppliers of trade credit during non-crisis periods, but they reduce trade
credit provision relatively more as a response to the aggregate collapse of bank credit.
We also use two indicators of liquidity (using cash stocks and cash flow) as an
alternative measure of firm's financial health. We find that firms with relatively lower
liquidity also show a larger decrease in their trade credit provision after crises. We avoid
endogeneity problem by using pre-crisis measures of financial health to study the post-
crisis trade credit policy.
While it is likely that some firms benefit from the crisis, on average most firms
are hurt by it. The result that firms with relatively weaker financial stand are more likely
to reduce trade credit provision, coupled with the prevalent deterioration in firms'
financial conditions around crises, suggests that the prolonged contraction in aggregate
trade credit could be, to a large extent, attributed to the supply effect.
The identification of the temporary increase of trade credit at the peak of financial
crisis is, however, different. Given the collapse of alternative sources of financing
(which suddenly dry out or become very expensive), it is natural to expect an increase in
trade credit demand in the immediate aftermath of the crisis, as firms move down in the
supply without regarding for a similar or even increased demand, this pattern of trade credit would indeed
imply that this instrument has only limited potential to compensate for bank credit.
3
pecking order.3 What we do not know is whether suppliers willingly allow customers to
take longer to repay (i.e. increasing supply to meet a higher demand) or they simply
cannot avoid payments delays by their customers (i.e. unintended increase in supply).
While we cannot decisively disentangle intended from unintended credit, our study
makes it apparent that trade credit can provide a very short-term source of "emergency
capital" due to the flexibility in credit terms, which allow for temporary extension of
credit maturity. In line with this argument, Cuņat (2002) argues that this assistance in
case of temporary illiquidity is one of the reasons why trade credit is so expensive.
In sum, our findings suggest that while trade credit could serve as a source of
very short-term "emergency credit", it could not fully substitute for contracted bank
credit in the longer aftermath of the crisis. We use the finding that firms with weaker
financial position are showing the highest decline in the provision of credit to their
customers to argue that the prolonged decline in trade credit provision we observe during
post crisis periods is most likely driven by a supply effect. .
Our results are broadly consistent with the redistribution view of trade credit.
This view posits that firms with better access to capital will redistribute the credit they
receive to less advantaged firms via trade credit. This view was first proposed by Meltzer
(1960) and further supported by Petersen and Rajan (1997) and Nilsen (2002) among
others. We note that for actual "redistribution" to take place some firms need to be able
to raise external finance, which they would pass on to less privileged firms.4 However,
during a financial crisis, alternative sources of finance are likely to dry out ­ stock
3Petersen and Rajan (1997)īs results imply that trade credit comes lower on the pecking order, suggesting
that "borrowing from trade creditors, at least for longer periods of time, is a more expensive form of
credit".
4For example, during monetary contractions in the US large firms increase the issuance of commercial
paper (Calomiris, Himmelberg and Wachtel (1995)) and accelerate bank credit growth while small firms
4
markets crash and foreign lenders and investors pull out their money. As all potential
sources of funds collapse there may be "nothing to redistribute" in terms of trade credit;
thus, contraction in the supply of formally intermediated funds lead to contraction in the
supply of trade credit finance. Consistent with this argument, we also find that countries
that experience a sharper decline in bank credit also experience a sharper decline in trade
credit.5
The remainder of the paper is as follows. Section II describes the data and
presents basic descriptive statistics and graphical analysis. In section III we discuss our
empirical strategy. In section IV we present the results and in Section V we conclude.
II. Data
II.1. Sample
We study two of the four major crises that occurred during the nineties, i.e. the
Mexican devaluation in late 1994-early 1995, and the South East Asia currency crisis in
mid-1997 including Indonesia, Korea, Malaysia, Philippines and Thailand.6 We use the
Worldscope database, which contains data on publicly traded firms around the world and
represents about 95% of the world's market value. Since it focuses mostly on the firms
for which there is a significant interest of international investors, the sample represents
the largest firms in each country. Our study excludes all financial firms.
reduce it (Gertler and Gilchrist (1994)). Such access to alternative sources of finance in the US is likely to
be behind the aggregate increase in trade credit (during monetary contractions) observed by Nilsen (2002).
5 Our results are also consistent with patterns observed in Demirguc-Kunt and Maksimovic (2001). They
find that the provision of trade credit across countries is positively correlated with the level of development
of financial intermediaries.
6 We are not able to include the analysis of the Russian default occurred in 1998 and the Brazilian
devaluation in early 1999 due to lack of data. We chose not to include information on China and Taiwan
because, even though they also suffered contemporaneous financial crisis, their impact is thought to be less
spread and not as pronounced.
5
The periods of financial crises are usually characterized by high rate of
liquidations and consolidations, which creates an unbalanced sample of firms
(Worldscope immediately delists all firms that go through any type of reorganization).
We present our results using this unbalanced sample to avoid an attrition bias, however,
all our results still hold when estimated on two alternative balanced samples.7 Table 1
Panel A reports the number of firms per country (counted at the crisis year) showing a
total sample of 890 firms.
II.2. Crisis timing
Table 1 Panel A reports the time of each crisis considered in our sample. To trace
out the timing before and after the crisis we create the timeline variable, which equals to
zero for the crisis year and takes values ­1, ­2, ­3 and 1, 2, 3 for the subsequent pre- and
post-crisis years respectively. The crisis year is defined for each firm at the fiscal year
ending within the 12-month interval after the crisis hit. Thus, for example, for the Asian
countries that were hit by the crisis in July 1997, the crisis year is defined as 1997 for
those firms which fiscal year ends from August to December 1997, and as 1998 for those
closing between January and July 1998.
II. 3. Dependent Variables
Our main variables of interest are accounts payables and accounts receivables,
which show the amount of trade credit that firms obtain from suppliers and provide to
customers, respectively. We scale these trade credit variables using sales (for receivables)
7The first sample, called "balanced 5", contains all firms that are present in the Worldscope database for at
least 2 years before and after the crisis (therefore covering 5 years around the crisis time); and the second
sample, "balanced 7", contains firms that are in the dataset for at least 7 years around the crisis time.
6
and cost of goods sold (for payables).8 These ratios capture the importance of trade credit
in the financing of the economic activity. One advantage of using ratios scaled by flow
variables is that these measures control for decline in economic activity (i.e. sales) that
are commonly associated with crises. Thus, whenever we find a declining ratio of
accounts receivables to sales, we know that accounts receivables have declined more than
sales, in percent terms.
There are two ways these ratios could be interpreted. If trade credit were extended
for the whole year, the ratio of receivables to sales would show what percent of sales is
done on credit. However, as trade credit usually has much shorter maturity, the
alternative interpretation of such a ratio is the number of days the customers take to repay
the credit (assuming all customers receive 100% of credit).9 In reality, the ratios are likely
to capture both, the percent of the goods sold on credit and the time it takes before the
credit is repaid. We follow tradition and multiply these ratios by 360 and interpret them
in terms of the number of days credit is extended and received (keeping the above caveat
in mind).
We also study net credit as the difference between receivables and payables,
again, scaled by sales. Firms that obtain more credit from suppliers are likely to be more
willing to extend credit to their customers. In this sense, net credit shows the relative
willingness to extend trade credit, net of the credit firms receive themselves.
Thus, we use the following set of three dependent variables:
TRECTOS: Trade Receivables / Total Sales
8It would be best to scale payables by the cost of materials purchased rather than total cost of the goods
sold, which includes labor costs, but such data is not available in our dataset (neither it is in most other
datasets used in previous papers). As a second best approach (also used in other studies) we scale payables
by the total cost.
7
TPAYTOC: Trade Payables / Cost of Goods Sold
NTCS: (Trade Receivables ­ Trade Payables)/ Total Sales
To ensure the robustness of our results, we examined the distribution of our key
variables and removed outliers. We removed all figures that appeared to be misreported
(such as negative numbers for trade credit or assets). For our trade credit ratios we
eliminated all values that implied trade credit of over one year long (this eliminated about
2-3% in the top tail of the distribution).
II.4. Descriptive analysis
Figure 1 presents the medians of trade credit ratios and the aggregate bank credit
figures around the crisis time. We observe that all trade credit ratios exhibit very similar
patterns ­ a slight increase in the crisis year and sharp declines in post crisis times. The
decline is most pronounced for net trade credit (from the highest to the lowest point the
drop is about 36%) and is much less dramatic for payables (with a change of only about
15%). We also see that trade payables start to go up in the second year after the crisis and
almost fully recover in the third year; receivables, on the other hand, stay low for all 3
years after the crisis.10 Interestingly, we observe that bank credit growth declines in the
two consecutive years after the crisis hit, clearly resembling the behavior of trade credit.
9In the US, for example, the most often term for extension of trade credit is about 30 days, and it varies by
industry, from about 10 days to 60 days (see Ng, Smith, and Smith (1999) and Mian and Smith (1992)).
Rarely, if ever, trade credit is extended for over 6 months.
10We also plot ratios scaled by assets, for comparison. We referred to them as trectoa, tpaytoa and ntca for
receivables, payables and net trade credit respectively. When the graphs are done separately for each
country, we find that most countries follow the same aggregate patterns. The most uniform behavior is
observed for receivables and net credit, while payables seem to exhibit more variation across countries.
Reproducing these graphs for mean ratios generates identical patterns.
8
Table 1, Panel C presents tests for statistical significance of the differences in
trade credit figures between crisis and post-crisis periods relative to the pre-crisis time.
The outcome is very consistent with our graphical analysis: both payables and receivables
show a significant increase in the crisis year, but only trade receivables and net credit
show a persistent decline in subsequent periods relative to the pre-crisis one. In the next
section we present the empirical models we use to study these patterns more formally.
III. Empirical Strategy
To study the effects of the crisis and the post-crisis on trade credit, we employ a
standard panel-data approach utilizing a firm fixed effects model. The fixed effects
capture the unobserved heterogeneity in the firm-specific (i.e. time-invariant) levels of
trade credit and allow us to isolate the effects of crisis and post-crisis relative to the pre-
crisis behavior.
III.1. Aggregate behavior
Our first test studies the aggregate behavior of firms during and after crises. To
implement it we define dummy variables for the crisis and post-crisis years, labeled as
CRISIS and POST, respectively. Combined with the fixed effects, these two dummies
capture the changes in trade credit relative to several years of pre-crisis data.
We use the following model:
TCit =i + 1 *CRISISct + 2 *POSTct + 3Xit +it (1)
9
Where TC is one of the three trade credit measures described in the data section,
X is a vector of firm and country time-specific control variables, is a firm fixed effect
and is an error term.
The CRISIS and POST-crisis dummies show the difference of trade credit ratios
in the crisis and post-crisis years relative to the average of pre-crisis years. In the reported
regressions we use two dummies: POST12 and POST3, which equal one for the first two
years and the third year following the crisis, respectively.11
We estimate the model using a technique that allows for extra care in treating the
error term. In particular, to make sure our results are robust to any possible temporal
correlation among the firms in each country-year period, we define a "clustering"
variable as a combination of country and time. Introducing a "cluster" option in our
methodology allows for an unspecified correlation structure of errors within the
"clusters". This is important since during the crisis and post-crisis years, the errors (i.e.
unexplained variation) could be correlated for the firms within the country. However, the
correlation might be different for pre-crisis, crisis or post-crisis years.
Causal factors that are either time-invariant (for example industry) or slowly
changing (for example size) would be mostly captured by the fixed effects. To control for
factors that vary significantly over time we use several control variables (included in the
vector X in model 1) that have been suggested by the trade credit literature.12 We use the
ratio of cash flow to total assets, the ratio of cash balances to total assets (measured at the
beginning of the period), and the firm-level sales growth rate in the previous year.
11We also run all regressions with a separate set of dummies (i.e. POST1, POST2 and POST3), all results
remain consistent and are available on request.
12 See Petersen and Rajan (1997) and Calomiris, Himmelberg and Wachtel (1995) for discussion of the
variable choice.
10
Finally, we control for the depreciation of the exchange rate to capture the country-time
differences in the magnitude of the crisis and recovery.
We removed observations with extreme values of sales growth, cash and cash
flows ratios (outside of the 1% tails in the distribution).13 Summary statistics for these
variables are reported in Table 1, Panel B.
III.2. Heterogeneous firm responses
To understand what is driving the aggregate results, we analyze firms'
heterogeneous responses to crisis events as a function of their relative financial positions.
We use several indicators of the firm's financial strength.
First, we use a ratio of short-term debt to assets. Firms with a high proportion of
short-term debt are likely to be the most disadvantaged by the crisis because they need to
roll-over their debt when it is either impossible or extremely costly. While high share of
short-term debt is not necessarily an indication of strong financial position before the
crisis, it is clearly an indication of weak financial position right after it.
Second, we use more "standard" proxies for liquidity position of the firm: firm's
cash flow and cash stock (both relative to firm's assets). We conjecture that firms with
larger pre-crisis stock of cash holdings (i.e. liquidity) as well as those with larger cash
flow generation can fall back on this cushion during the crisis times, and are therefore
likely to be in a better financial position to provide trade credit to their customers (as well
as to avoid making use of expensive financing from their suppliers).
To study differences in firm's responses to crisis, we interact our financing
variables with crisis and post crisis dummies. One may worry about the endogeneity of
11
contemporaneous financing variables, such as short-term debt and liquidity measures,
given that they are likely to be affected by the firm's trade credit policy. To address this
potential concern, we use pre-crisis levels of our financing variables in the interaction
terms. Thus, we study responses to crisis in firms with different pre-crisis financial
health.
We use the following extension of the model in equation (1):
TC =i + 1 *CRISISct + 2 *POSTct +
+ 3FINi (-1)*CRISISct + FINi 4 (-1)*POSTct + Xit +it (2)
where FINi(-1) represents one of the above described indicators of financial position.
Since FIN is not time-varying (because it is measured at the pre-crisis level), the level of
FIN is subsumed into the fixed effects.
In this model, the effect of crisis on TC depends on the level of financial
indicator, FIN. For firms with FIN equal to zero, the difference in trade credit ratios
during crisis and post-crisis years (relative to pre-crisis average) will be given by 1 and
2, respectively; same as in model (1). However, the effect of crisis on TC will vary for
firms with different levels of FIN: e.g. for firms with a financial indicator equal to F, the
difference in trade credit levels during the crisis (relative to pre-crisis) will be given by 1
+ 3 * F.
III.3. Heterogeneous country-level response to crisis
13To preserve our sample size we do not drop outlier observations but simply set them to missing, As a
result the number of actual observations used is somewhat different from model to model.
12
Our final test explores the variation in bank credit growth across years and countries in
our sample.14 To test the effect of bank credit growth on trade credit behavior before,
during and after the crisis, we use the following model:
TCit = i + 1 *CRISISct + POSTct + 3CREDITGRct
2
+ 4CREDITGRct *CRISISct + 5CREDITGRct *POSTct + Xit +it (3)
Where CREDITGR is the country-year growth rate in the private credit to GDP
ratio (obtained from the IFS). The coefficients 3, 4 and 5 show the reaction of trade
credit to bank credit growth during the pre-crisis, crisis and post-crisis respectively. As
before, we allow for the same set of control variables for robustness checks. Finding
positive coefficients on 3, 4 and 5 would suggest that increase in bank credit leads to
more trade credit provided and/or received by firms in our sample, consistent with the
redistribution story.
IV. Results
IV.1. Aggregate patterns
We present our main results using the unbalanced sample.15 All tables show the
first three regressions without control variables and the following three including the set
of time varying control variables.
Table 2 presents our basic results. The coefficients on the crisis and post-crisis
dummies show the difference in trade credit during these stages relative to the pre-crisis
14Since we only have 6 countries and at most 7 years, we are concerned about the degrees of freedom.
Therefore these results should be interpreted with caution.
15Results for the "balanced 5" and "balanced 7" samples are very similar and are available on request.
13
period. We observe the same pattern shown in the graphical analysis. In particular,
accounts receivables increase immediately after the crisis and then drop sharply in the
post-crisis time. Account payables, however, after increasing at the peak of the crises, do
not exhibit a significant decline (relative to pre-crisis figures). In terms of the magnitude,
we observe that during the crisis year both payables and receivables increase by about a
week, relative to the pre-crisis period. In the post-crisis, however, the receivables drop by
the same amount in the first two years (again, relative to pre-crisis figures) and continue
dropping well into the third year.16
As discussed earlier, there could be several alternative explanations for these
patterns. On the one hand, the decline in trade credit provided could be the result of a
supply effect: the firms that suffer from lack of access to intermediated credit reduce the
supply of credit they are willing to provide to their customers. On the other hand, this
pattern would be also consistent with a demand-side story: the customers of our firms
become less willing to take on more credit. To understand these aggregate patterns,
below we explore the firms' heterogeneous responses to crisis.
We focus on deciphering the reasons for the aggregate decline in trade credit
provided by our firms in the post-crisis years as this result is both more surprising and
more prolonged (as it continues for several years after the crisis) than the short-term
increase in trade credit provided during the crisis year. In addition our data are not well-
suited to study the reasons for temporary increase in trade credit (for example, we do not
16 Because the trade credit maturity is usually much shorter than one year, the temporary spike in both
ratios is not simply caused by a mechanical relationship due to contraction in the scaling factor (i.e. sales or
cost of goods sold). Suppose the crisis occurred several months before the end of the fiscal year and the
maturity of receivables is less than one year. Then, the mechanical relationship would actually run in
reverse ­ if accounts receivable decline as much as sales do, the ratio of receivables to sales would go down
because the numerator will reflect a post-crisis low level of receivables (extended on post-crisis low level
of sales), while the denominator would reflect the whole year of sales (i.e. high pre-crisis level and low
post-crisis level).
14
have the data on non-performing loans, or the data on reclassified receivables), while we
do have enough data to shed some light on the reasons for decline in trade credit after the
crisis.
IV.2. Heterogeneous firm responses
In this section, we study firmsī trade credit policy as a function of their relative financial
health. The supply-driven reason for decline in trade credit provision after the crisis
would be caused by the unwillingness (or inability) of suppliers to provide credit to their
customers. In this case, we would expect that those firms in more difficult financial
position would be the most likely to cut the supply of credit to their customers. The
supply-driven reason for reduction in trade credit provided to firms' customers would
imply that such a reduction is relatively higher for firms with weaker pre-crisis financial
conditions. This argument forms our main identification strategy.
IV.2.1. Short-term debt.
As suggested earlier, firms that enter the crisis with a high proportion of short-
term debt are likely to be particularly disadvantaged by the credit crunch, because of the
higher costs of short-term debt and difficulties in rolling it over. Even though the
contemporaneous short-term debt to assets ratio is likely to be endogenous, we initially
run the regressions using this figure, because it allows us to observe the effect of short-
term debt in the pre-crisis period. We estimate model (2) and present results in Table 3,
Panel A.
The coefficient on Stdtoa shows the effect of short-term debt on firmsī pre-crisis
trade credit provision: We find that firms with higher percent of short-term debt provide
more credit to their customers during non-crisis times. To get a sense on the order of
15
magnitude in which the reliance of short-term debt affects trade credit policy, we focus
on the results presented in Column 4 (which includes controls for time-invariant firm
characteristics). The coefficients imply that firms with a ratio of short-term debt to assets
equal to one17 extend credit for about 50 more days relative to firms with zero short-term
debt. Alternatively, an increase in short-term debt to assets by one standard deviation
(0.18, as reported in Table 1, Panel B), would imply an increase in trade credit provided
of about 9 days, which is an economically significant effect.
The CRISIS coefficient shows the effect of crisis on firms with zero short-term
debt: these firms increase trade credit provided by about 11 days during the crisis,
relative to the pre-crisis period. The coefficient on the interaction of Stdtoa and CRISIS
shows how the response to crises changes as firms increase reliance on short-term debt.
We find that firms with the maximum ratio of short-term debt to assets actually shorten
the credit provided by about 16 days, relatively to pre-crisis levels (calculated as 11.32-
27.81).
Finally, the post-crisis dummies (POST12 and POST3) show the difference in the
post-crisis trade credit provision relative to pre-crisis for firms with zero short-term debt.
Interestingly, these dummies are not significant, which suggests that firms with zero
short-term debt do not experience a decline in trade credit provided in the aftermath of
the crisis (relative to pre-crisis period). Thus, the decline in the aggregate trade credit
observed in the post-crisis could be mostly attributed to firms with some short-term debt.
Looking at the interaction of Stdtoa and post-crisis dummies we analyze the
differential effect of post-crisis on firms with different levels of short-term debt. The
results imply that firms with a maximum amount of short-term debt cut the credit
17We use this extreme ratio of Stdtoa for illustration only. In our sample the maximum value for Stdtoa is
16
provided to their customers by about 50 days in the first two years after the crisis relative
to what these firms provided in the pre-crisis period.18 Hence, firms without short-term
debt do not experience a significant decline in the credit provided to their customers in
the post-crisis, while firms with more short-term debt experience a significant decline.
The above discussion focused on the effects of crisis and post-crisis on firms with
different levels of short-term debt. An alternative interpretation could focus on the effects
of short-term debt in different time periods. Thus, we see that while at the pre-crisis firms
with a maximum amount of short-term debt extend credit for 50 more days (relative to
firms with zero short-term debt), during the crisis these firms extend credit for only 21
more days (i.e. 48.99-27.81); finally, after the crisis, the trade credit policy of these firms
is no longer different from that of firms with zero short-term debt (i.e. 48.99 ­ 50.68). In
other words, all the extra credit that firms with a maximum amount of short-term debt
provided at the pre-crisis (relative to firms with zero short-term debt) is eliminated in the
post-crisis.
In addition to the effect of short-term debt on receivables, we see that firms with
more short-term debt experience an increase in payables during and after the crisis. These
results are consistent with the idea that firms with high short-term debt have a preferable
financial position before the crisis (and therefore provide more credit to their customers)
and a disadvantaged financial position after the crisis hit (which leads them to provide
less credit to their customers and rely more on credit from suppliers).
As we already suggested, potential endogeneity could arise because short-term
debt could be influenced by the trade credit policy. To control for such potential
equal to 0.99.
18We calculate this effect by taking the coefficient on Post12*Stdtoa interaction, which is equal to ­50.68
and assuming that Post12 dummy is equal to zero, since it is not significant.
17
endogeneity, we re-estimate our model using only pre-crisis values of short-term debt in
interactions. The results are reported in Table 3, Panel B. There, the pre-crisis level of
short-term debt is subsumed in fixed effects (because it is no longer time-varying) and we
are only able to see the differential responses to the crisis events. We find the same
response: firms with high pre-crisis level of short-term debt decrease trade credit
provision during and after crises and increase reliance on credit from suppliers.
These results suggest that firms with high pre-crisis level of short-term debt are in
a more difficult financial position after the crisis and, therefore, the decrease in trade
credit they provided to their customers is driven by their unwillingness (or inability) to
extend (i.e. supply) more credit. It is quite unlikely that the decrease in customer's
demand for trade credit after the crisis would be related to the firm's financial position
before the crisis. This allows us to rule out the demand-driven explanation for the decline
in aggregate trade credit in favor of the supply-driven story. In other words, the
disruption to the redistribution mechanism typically provided by trade credit comes from
the special difficulties inflicted upon the most traditional suppliers of this type of credit,
namely, the firms with higher exposures to short-term borrowing.
IV.2.2. Cash flows and liquidity
To test the robustness of our previous result, we use two other indicators of firmsī
financial health. Firms that arrive at the crises with a large liquidity cushion (represented
either by larger cash stocks or cash flow generation) are better fitted to financially
support profitable commercial operations (by extending more credit to their customers) as
well as to temporarily reduce reliance on credit from suppliers.
18
In Table 4 we estimate model (2), using the pre-crisis cash flow to net assets ratio
as an alternative indicator of firmsī financial position. The interaction terms are positive
and highly significant for receivables, with or without the subset of additional control
variables. Thus, firms with high pre-crisis cash flow generation provide more financing to
their customers both during and after crises. More specifically, the magnitudes of the
coefficients imply that firms with cash flow ratios below the 86 percentile (about .13 in
the data) reduce the credit provided to their customers after the crisis, while firms with
cash flows ratios above the 86 percentile actually increase the credit they provide in the
post-crisis. There is no evidence that firms with high cash flow make less use of trade
credit during and after crises.
The next test includes the interaction of the crisis and post-crisis indicators with
the pre-crisis cash to assets ratio. Results are presented in Table 5. There we observe that
firms with higher pre-crisis cash to assets ratios tend to give more credit to their
customers during crisis and post-crisis times. Finally, we also find that firms with larger
stocks of cash rely less on credit from suppliers; this is only true, however, for the two
years following the crisis hit.
Since we construct the interactions using pre-crisis cash flows and cash stocks,
the results imply that firms that come to the crisis with a strong financial position are less
affected by the crisis and consequently provide more credit to their customers, relative to
firms that arrived at the crises with a weaker financial position. These two sets of results
reinforce our conclusion above that the decline of trade credit observed after crises is
mainly driven by a supply effect.
IV.3. Heterogeneous country-level response to crisis: Bank Credit Growth
19
To deepen our understanding of trade credit patterns during crisis and post-crisis
times, we study the differences in response of trade credit to aggregate bank credit
behavior. A further confirmation of the supply side story would suggest that countries
that experience a sharper decline in bank credit should also experience a sharper decline
in trade credit. In other words, the supply of intermediated credit would affect the supply
of trade credit.
We use the model (3) and report the results in Table 6.19 First, we observe a clear
positive relationship between bank credit growth and extension of trade credit during the
crisis period. Again, the positive response is the most significant for receivables, and not
so much for payables, where coefficients are positive but non-significant. We also find
that the post-crisis drop in trade credit provided by the firms in our sample (and therefore
the drop in the net credit) is sharper for countries that experienced larger contractions in
bank credit. Despite the potential limitations of this analysis, the results are consistent
with the supply-driven explanation: Contractions in bank credit are at least partially
responsible for contractions in trade credit. Since most of the contraction in bank credit is
likely to come in the form of short-term debt not being rolled over (since long-term debt
would not be immediately affected by the crisis), this result bodes well with our earlier
results for firms with higher shares of short-term debt. These results are also consistent
with Demirguc- Kunt and Maksimovic (2001) and Meltzer (1960).
V. Conclusions
We study the behavior of trade credit around the time of financial crises. The
simple inspection of trade credit patterns shows a significant increase of trade credit at
20
the peak of financial crises, followed by a subsequent collapse of this source of financing
right after the crisis events. This is also confirmed in a more thorough regression
analysis.
Given that these findings could be explained by either supply- or demand-side
stories, we study firmsī heterogeneous responses to crises, and characterize changes of
trade credit policy around crises as a function of firmsī relative financial health.
Specifically, we analyze two alternative indicators of firms' financial strength: reliance
on short-term debt and liquidity.
We find that before the crises, firms with high proportion of short-term debt are
significant providers of trade credit. However, after the crisis, these firms sharply cut the
amount of credit they provide and increase reliance on credit from suppliers. In other
words, what is a preferred financial position before the crisis (i.e. short-term debt) turns
into a heavy disadvantage right after it, with the corresponding change in trade credit
policy. We also find some evidence that more liquid firms (i.e. those with high cash stock
or cash flow) extend more credit to their customers and rely less on credit from their
suppliers.
Given that the reduction of trade credit provision is significantly higher for firms
exhibiting weaker financial conditions, we conclude that the contraction of such a credit
is most likely driven by a supply effect. Our findings show that even though trade credit
could potentially serve a role of emergency assistance, this assistance is very short-term.
In the long aftermath of the crisis, trade credit contracts as a result of overall shortage of
funds and difficulties experienced by firms with high reliance on short-term debt. Our
19We use the country and time variation in the credit growth. However, since we only have 6 countries and
7 periods the degrees of freedom might be a concern. Therefore these results should be interpreted with a
caution and would require more scrutiny in the subsequent research.
21
results highlight the importance of the aggregate bank credit availability, especially
during the times of the crisis.
Although a useful start, our paper leaves many areas for future research. Our data
includes only few crises in a small set of countries and consequently, leaves us with some
concern regarding degrees-of-freedom. In addition, the patterns observed for largest
publicly traded firms may not generalize to the rest of the firms' population. More
research is needed to test whether the patterns we find hold for different firms' sizes and
are robust in a different sample of crisis episodes. Finally, our paper does not answer the
question of whether trade credit helps the firms to survive the crisis, or increase market
share and profitability. These are important questions to warrant more future research on
this topic with better-suited data.
22
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24
TPay/CGS TPay/Assets TRec/Sales TRec/Assets
54.26 6.88 90.45 16.67
/Sales
ec
TPay/CGS TPay/Assets TR TRec/Assets
44.29 5.80 66.49 13.12
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3
Timeline Timeline
Trade Payables Trade Receivalbles
Media Mea
NTC/Sales NTC/Assets Bank Credit Gwt
n Bank Credit Gwt
n
45.65 8.68 0.12 0.10
t t
sle ts Gw Gw
editrC editrC
NTC/Sa NTC/Asse
Bank Bank
29.39 5.49 -0.05 -0.17
-3 -2 -1 0 1 2 3 -3 -2 -1 0 1 2 3
Timeline Timeline
Net Trade Credit Bank Credit Growth
Median Trade Credit and Bank Credit Growth
Table 1 Panel A: Number of Observations by Country
This table presents the number of observations by country, based on the number of non-missing
values of the variable Trectos (computed as trade receivables / net sales ), counted at the crisis
time. The second column presents the Crisis Date for each country.
Country Number of Crisis
Observations Date
Indonesia 102 Jul-97
Korea 236 Oct-97
Malaysia 261 Jul-97
Mexico 59 Dec-94
Philippines 54 Jul-97
Thailand 178 Jul-97
Total 890
Table 1 Panel B: Summary Statistics
Trectos is measured as trade receivables / net sales, Tpaytoc is trade payables / cost of goods
sold, and Ntcs is net trade credit (i.e. receivables minus payables) / net sales. Cfw is operating
cash flow to assets, Growth is computed as lagged growth of sales, Cashta is cash/assets,
Exchrgr is the country's devaluation of the currency in the last year, and Stdtoa is short term
debt/total assets. The sample is the unbalanced panel of firms three years before and after each
crisis.
Variable N. Obs. Mean Min Median Max St. Dev.
Dependent Variables
Trectos 5552 94.03 0.00 80.86 290.67 60.68
Tpaytoc 5554 57.54 0.00 49.59 210.00 38.97
Ntcs 5325 51.42 -114.15 41.43 275.75 56.88
Control Variables
Cfw 5651 0.05 -0.58 0.06 0.31 0.11
Growth 5255 0.05 -0.90 0.05 0.90 0.26
Cashta 5868 0.10 0.00 0.06 0.94 0.11
Exchrgr 5441 0.10 -0.24 0.03 1.24 0.23
Stdtoa 5755 0.21 0.00 0.17 0.99 0.18
26
Table 1 Panel C: ANOVA Analysis
This table reports the difference in means between the two periods and the corresponding p-
values (computed using the Bonferroni-adjusted significance levels). Trectos is computed as
trade receivables / net sales, Tpaytoc is trade payables / cost of goods sold, and Ntcs is net trade
credit / net sales. The sample is the unbalanced panel of firms three years before and after each
crisis.
Variable Crisis vs. Pre-Crisis Post-Crisis-vs. Pre-Crisis
Trectos 5.2930 -13.2226
0.080 0.000
Tpaytoc 6.9031 0.5190
0.000 1.000
Ntcs 0.0496 -14.93
1.000 0.000
27
Table 2: Trade Credit in Aggregate
The dependent variables are the trade credit measures: Trectos is trade receivables / net sales,
Tpaytoc is trade payables / cost of goods sold, and Ntcs is net trade credit (i.e. receivables minus
payables) / net sales. Crisis is a dummy for crisis year, Post12 is a dummy for first two years
after the crisis and Post3 is dummy for third year after the crisis. Cfw is operating cash flow to
assets, Growth is lagged growth of sales, Cashta is cash/assets measured at the beginning of the
year, Exchrgr is the rate of currency devaluation. The models are estimated with firm-fixed
effects (see model (1) in the paper) using the unbalanced sample. The standard errors were
obtained using clustering on country and time as explained in the paper. ***, ** and * represent
coefficients significant at the 1%, 5% and 10% level. Absolute value of t-stats in brackets.
(1) (2) (3) (4) (5) (6)
Trectos Tpaytoc Ntcs Trectos Tpaytoc Ntcs
Crisis 7.58** 7.02** 2.07 6.85** 7.21** 1.64
[2.04] [2.12] [0.82] [2.03] [2.11] [0.63]
Post12 -6.29** 0.31 -7.56*** -7.57** -0.31 -8.33***
[2.02] [0.22] [2.63] [2.28] [0.19] [2.88]
Post3 -13.94*** 1.21 -15.04*** -14.16*** 1.15 -14.63***
[3.19] [0.26] [4.82] [3.73] [0.28] [5.09]
Cfw 2.89 -9.26* 18.12**
[0.28] [1.66] [2.09]
Growth -4.69 -6.48*** 0.08
[1.40] [3.11] [0.03]
Cashta 4.41 3.31 4.38
[0.49] [0.37] [0.33]
Exchrgr 7.82 -5.13 11.02***
[1.58] [1.23] [3.07]
Observations 5552 5554 5325 4256 4244 4091
R-squared 0.74 0.63 0.72 0.79 0.69 0.76
28
Table 3: Trade Credit and Short-Term Debt
The dependent variables are the trade credit measures. See header to Table 2 for variable
definitions. The Stdtoa is the ratio of short-term debt to assets measured at the individual firm
level. This table shows the interactions of Stdtoa with the Crisis, Post12 and Post3 dummies.
The models are estimated with firm-fixed effects (see model (2) in the paper), using the
unbalanced sample. The standard errors were obtained using clustering on country and time as
explained in the paper. ***, ** and * represent coefficients significant at the 1%, 5% and 10%
level. Absolute value of robust t-stats in brackets.
Panel A: Contemporaneous levels of short-term debt.
(1) (2) (3) (4) (5) (6)
Trectos Tpaytoc Ntcs Trectos Tpaytoc Ntcs
Crisis 9.56** 5.02 4.31 11.32*** 3.95 9.02***
[2.24] [1.22] [1.31] [3.08] [0.94] [3.12]
Post12 3.32 -0.01 3.29 3.1 -2.59 5.16*
[1.04] [0.01] [1.15] [0.83] [1.19] [1.73]
Post3 -2.4 -0.05 -2.45 -1.14 -0.93 0.99
[0.59] [0.01] [0.95] [0.27] [0.17] [0.36]
Stdtoa 49.91*** 6.82 50.07*** 48.99*** -11.11 64.57***
[4.63] [0.84] [5.56] [4.31] [1.31] [7.69]
Crisis * Stdtoa -19.22** 6.14 -20.23** -27.81*** 14.55** -41.00***
[2.03] [0.84] [2.20] [3.74] [2.04] [6.42]
Post12 * Stdtoa -47.55*** 0.28 -51.36*** -50.68*** 12.03* -64.02***
[4.70] [0.04] [5.34] [4.54] [1.65] [6.83]
Post3 * Stdtoa -62.92*** 5.9 -66.21*** -66.66*** 9.27 -78.57***
[5.09] [0.49] [4.68] [5.27] [0.89] [6.66]
Cfw -1.59 -10.37* 16.60*
[0.15] [1.89] [1.86]
Growth -4.66 -6.17*** -0.28
[1.29] [2.91] [0.09]
Cashta 6.62 4.42 7.13
[0.80] [0.49] [0.53]
Exchrgr 7.25 -5.25 10.05***
[1.56] [1.31] [3.23]
Observations 5455 5460 5243 4194 4187 4036
R-squared 0.75 0.64 0.73 0.8 0.69 0.78
29
Panel B: Pre-crisis level of short-term debt.
The Stdtoa1 is the firm-level ratio of short-term debt to assets computed one year before the
crisis.
(1) (2) (3) (4) (5) (6)
Trectos Tpaytoc Ntcs Trectos Tpaytoc Ntcs
Crisis 11.22*** 4.34 7.67** 14.36*** 3.85 13.08***
[2.81] [1.18] [2.23] [3.26] [1.01] [3.55]
Post12 5.75 -0.64 5.93* 5.19 -3.78 7.51**
[1.58] [0.28] [1.90] [1.14] [1.54] [2.12]
Post3 -0.22 6.94 -3.26 0.03 3.28 0.63
[0.04] [1.13] [0.81] [0.01] [0.55] [0.18]
Crisis*Stdtoa1 -17.04* 13.67*** -27.12*** -33.90*** 15.93*** -52.58***
[1.85] [2.96] [3.20] [2.82] [3.08] [5.47]
Post12*Stdtoa1 -61.93*** 4.58 -69.33*** -61.01*** 16.46** -75.25***
[5.64] [0.73] [7.05] [4.41] [2.33] [6.66]
Post3*Stdtoa1 -72.02*** -29.20** -61.93*** -70.23*** -11.88 -74.67***
[3.94] [2.20] [4.20] [3.89] [1.01] [4.86]
Cfw 4.18 -8.17 19.45**
[0.40] [1.44] [2.21]
Growth -5.25 -6.83*** -0.54
[1.50] [3.18] [0.19]
Cashta 4.38 3.02 4.03
[0.49] [0.33] [0.30]
Exchrgr 7.23 -4.81 10.28***
[1.40] [1.18] [3.02]
Observations 5385 5377 5168 4183 4170 4021
R-squared 0.75 0.63 0.72 0.79 0.69 0.77
30
Table 4: Trade Credit and Cash Flows
The dependent variables are the trade credit measures. See header to Table 2 for variable
definitions. The Cfw1 is the measure of cash flow to total assets computed at the year prior to the
crisis. This table shows the interactions of Cfw1 with the Crisis, Post12 and Post3 dummies.
The models are estimated with firm-fixed effects (see model (2) in the paper) using an
unbalanced sample. The standard errors were obtained using clustering on country and time as
explained in the paper. ***, ** and * represent coefficients significant at the 1%, 5% and 10%
level. Absolute value of robust t-stats in brackets.
(1) (2) (3) (4) (5) (6)
Trectos Tpaytoc Ntcs Trectos Tpaytoc Ntcs
Crisis 4.54 6.72** -2.37 3.95 8.66*** -4.46*
[1.25] [2.13] [1.04] [1.32] [2.76] [1.82]
Post12 -16.29*** -0.27 -18.05*** -17.49*** 0.72 -19.92***
[4.64] [0.15] [5.69] [5.00] [0.28] [6.48]
Post3 -25.76*** -4.08 -24.46*** -27.73*** -3.13 -26.65***
[4.84] [0.70] [8.16] [5.83] [0.59] [9.81]
Crisis * Cfw1 50.83** 2.12 68.02*** 48.75* -21.71 84.75***
[2.21] [0.11] [3.97] [1.74] [1.11] [3.19]
Post12 * Cfw1 140.93*** 6.24 144.94*** 131.49*** -16.38 151.32***
[5.84] [0.29] [6.62] [4.83] [0.67] [5.92]
Post3 * Cfw1 159.10*** 72.03* 123.73*** 169.35*** 51.2 144.48***
[4.60] [1.89] [6.19] [4.97] [1.40] [7.07]
Growth -4.26 -6.59*** 1.03
[1.23] [2.83] [0.33]
Cashta 2.89 2.43 3.54
[0.31] [0.28] [0.26]
Exchrgr 6.27 -3.45 8.65**
[1.10] [0.91] [2.18]
Observations 5330 5326 5122 4291 4289 4122
R-squared 0.75 0.63 0.72 0.78 0.68 0.76
31
Table 5: Trade Credit and Cash Stock
The dependent variables are the trade credit measures. See header to Table 2 for variable
definitions. The Cashta1 is the ratio of cash to assets computed at the pre-crisis time. This table
shows the interactions of Cashta1 with the Crisis, Post12 and Post3 dummies. The models are
estimated with firm-fixed effects (see model (2) in the paper) using an unbalanced sample. The
standard errors were obtained using clustering on country and time as explained in the paper.
***, ** and * represent coefficients significant at the 1%, 5% and 10% level. Absolute value of
robust t-stats in brackets.
(1) (2) (3) (4) (5) (6)
Trectos Tpaytoc Ntcs Trectos Tpaytoc Ntcs
Crisis 6.36* 7.16** 0.56 3.72 7.45** -1.79
[1.90] [2.27] [0.23] [1.39] [2.33] [0.75]
Post12 -9.77*** 1.39 -11.63*** -10.45*** 2.29 -12.97***
[2.64] [0.73] [3.24] [2.86] [1.40] [3.80]
Post3 -18.71*** 1.49 -19.60*** -18.57*** 2.53 -20.47***
[4.16] [0.25] [6.44] [5.00] [0.49] [7.11]
Crisis*Cashta1 16.03 -1.63 18.98** 37.39** -4.68 41.98**
[1.21] [0.15] [2.17] [1.98] [0.32] [2.42]
Post12*Cashta1 36.08** -11.84* 41.60*** 34.50* -31.08*** 54.68***
[2.57] [1.67] [2.79] [1.84] [2.81] [2.82]
Post3*Cashta1 47.44*** -1.49 43.81** 48.94** -16.17 64.22***
[2.82] [0.12] [2.57] [2.29] [1.18] [2.80]
Cfw 4.5 -9.22* 20.53**
[0.43] [1.73] [2.28]
Growth -5.17 -6.79*** -0.53
[1.49] [3.20] [0.17]
Exchrgr 7.72 -4.91 10.90***
[1.51] [1.25] [2.95]
Observations 5388 5377 5171 4184 4170 4022
R-squared 0.74 0.63 0.72 0.79 0.69 0.76
32
Table 6: Trade Credit and Bank Credit Growth
The dependent variables are the trade credit measures. See header to Table 2 for variable
definitions. The Creditgr is the annual growth of bank credit to the private sector scaled by GDP,
for each country-year. This table shows the interactions of Creditgr with the Crisis, Post12 and
Post3 dummies. The models are estimated with firm-fixed effects (see model (3) in the paper)
using an unbalanced sample. The standard errors were obtained using clustering on country and
time as explained in the paper. ***, ** and * represent coefficients significant at the 1%, 5% and
10% level. Absolute value of robust t-stats in brackets.
(1) (2) (3) (4) (5) (6)
Trectos Tpaytoc Ntcs Trectos Tpaytoc Ntcs
Crisis -3.33 0.82 -4.1 -5.44 4.34 -8.96
[0.45] [0.15] [0.70] [0.62] [0.68] [1.24]
Post12 -8.33** 0.55 -9.91*** -10.96** 0.57 -12.39***
[2.34] [0.31] [2.66] [2.29] [0.22] [2.75]
Post3 -18.86*** 0.31 -19.28*** -19.86*** 0.1 -19.44***
[4.60] [0.07] [6.46] [4.77] [0.02] [5.77]
Credgr -48.10* -10.15 -41.01* -66.37* -14.03 -55.54*
[1.81] [0.74] [1.79] [1.76] [0.57] [1.87]
Crisis*Credgr 113.33** 63.58* 65.67 140.53** 38.19 116.54**
[2.06] [1.90] [1.35] [2.04] [0.91] [1.99]
Post12*Credgr 67.79*** 20.19 52.05** 78.77** 26.67 58.11**
[2.63] [1.54] [2.38] [2.22] [1.13] [2.09]
Post3*Credgr 16.67 6.44 8.5 39.43 8.15 31.52
[0.50] [0.26] [0.31] [0.93] [0.25] [0.92]
Cfw 5.51 -6.87 18.61**
[0.60] [1.30] [2.33]
Growth -3.77 -5.62** 0.42
[1.09] [2.49] [0.15]
Cashta 5.08 3.29 5.01
[0.55] [0.37] [0.37]
Exchrgr 5.95 -7.79** 11.28**
[1.00] [2.27] [2.18]
Observations 5529 5531 5302 4256 4244 4091
R-squared 0.75 0.63 0.72 0.79 0.69 0.77
33